System and method for determining a nucleotide sequence

ABSTRACT

Described herein are systems and processes for designing the sequence of one or more interacting nucleic acid strands intended to adopt a target secondary structure at equilibrium. The target secondary structure is decomposed into a binary tree and candidate mutations are evaluated on leaf nodes of the tree. During a process of leaf optimization, defect-weighted mutation sampling is used to select each candidate mutation position with a probability proportional to its contribution to an ensemble defect of the leaf. Subsequences of the tree are then merged, moving up the tree until a final nucleotide sequence of interest is determined that has the target secondary structure at equilibrium.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a divisional of U.S. application Ser. No.13/079,747, filed Apr. 4, 2011, which claims priority to U.S.Provisional Application No. 61/320,863 filed on Apr. 5, 2010, all ofwhich are incorporated by reference in their entirety.

REFERENCE TO SEQUENCE LISTING

The present application includes a Sequence Listing in Electronicformat. The Sequence Listing is provided as a file entitledCALTE.068D1_Sequence.txt, created Feb. 20, 2013, which is approximately1 kb in size. The information in the electronic format of the sequencelisting is incorporated herein by reference in its entirety.

GOVERNMENTAL RIGHTS

This work was supported federal by funding from the National ScienceFoundation under grant numbers NSF-CCF-0832824 (The MolecularProgramming Project) and NSF-CCF-CAREER-0448835. The government hasrights in this invention.

BACKGROUND OF THE INVENTION

The programmable chemistry of nucleic acid base pairing enables therational design of self-assembling molecular structures, devices, andsystems. However, the ability to specify a target nucleic acidstructure, and then design a nucleic acid sequence that will adopt thistarget structure is still challenging and computationally intensive.

Secondary Structure Model

For an RNA strand with N nucleotides, the sequence, φ, is specified bybase identities φ_(i) ∈ {A, C, G, U} for i=1, . . . , N (T replaces Ufor DNA). The secondary structure, s, of one or more interacting RNAstrands can be defined by a set of base pairs (each a Watson Crick pair[A-U or C-G] or wobble pair [G-U]). Using the set of base pairs, apolymer graph for a secondary structure can then be constructed byordering the strands around a circle, drawing the backbones insuccession from 5′ to 3′ around the circumference with a nick betweeneach strand, and drawing straight lines connecting paired bases.

A secondary structure is “pseudoknotted” if every strand orderingcorresponds to a polymer graph with crossing lines. A secondarystructure is connected if no subset of the strands is free of theothers. An “ordered complex” corresponds to the unpseudoknottedstructural ensemble, Γ, comprising all connected polymer graphs with nocrossing lines for a particular ordering of a set of strands. For asecondary structure, s ∈ Γ, the free energy, ΔG(φ, s), is calculatedusing nearest-neighbor empirical parameters for RNA in 1M Na⁺⁵ or forDNA in user-specified Na⁺ and Mg⁺⁺ concentrations. This physical modelprovides the basis for rigorous analysis and design of equilibriumbase-pairing in the context of the free energy landscape defined overensemble Γ.

Characterizing Equilibrium Secondary Structure

Using this secondary energy model, the equilibrium of a nucleic acidcomplex can be characterized. The equilibrium state of nucleic acidcomplex can be determined by calculating the partition function

${Q(\varphi)} = {\sum\limits_{s \in \Gamma}^{{- \Delta}\; {{G{({\varphi,s})}}/k_{B}}T}}$

over the unpseudoknotted structure ensemble Γ. Using the partitionfunction, it is possible to then evaluate the equilibrium probability

${p\left( {\varphi,s} \right)} = {\frac{1}{Q(\varphi)}^{{{- \Delta}\; {{G{({\varphi,s})}}/k_{B}}T},}}$

of any secondary structure s ∈ Γ. Here, k_(B) is the Boltzmann constantand T is temperature. The secondary structure with the highestprobability at equilibrium is the minimum free energy (MFE) structure,satisfying

${s^{MFE}(\varphi)} = {\arg \; {\min\limits_{s \in \Gamma}{\Delta \; {{G\left( {\varphi,s} \right)}.}}}}$

The equilibrium structural features of ensemble Γ are quantified by thebase-pairing probability matrix, P(φ), with entries P_(ij) (φ) ∈ [0, 1]corresponding to the probability,

${{P_{i,j}(\varphi)} = {\sum\limits_{s \in \Gamma}{{p\left( {\varphi,s} \right)}{S_{i,j}(s)}}}},$

that base pair i·j forms at equilibrium. Here, S(s) defines a structurematrix with entries S_(ij)(s) ∈ {0, 1}. If structure s contains pairi·j, then S_(ij)(s)=1, otherwise S_(ij)(s)=0. For convenience, thestructure and probability matrices are augmented with an extra column todescribe unpaired bases. The entry S_(i,N+1)(s) is unity if base i isunpaired in structure s and zero otherwise; the entry P_(i,N+1)(φ) ∈ [0,1] denotes the equilibrium probability that base i is unpaired overensemble Γ. Hence the row sums of the augmented S(s) and P(φ) matricesare unity.

The distance between two secondary structures, s₁ and s₂, is the numberof nucleotides paired differently in the two structures:

${d\left( {s_{1},s_{2}} \right)} = {N - {\underset{1 \leq j \leq {N + 1}}{\sum\limits_{1 \leq i \leq N}}{{S_{i,j}\left( s_{1} \right)}{{S_{i,j}\left( s_{2} \right)}.}}}}$

The discrete delta function is defined as

$\delta_{s_{1},s_{2}} = \left\{ \begin{matrix}{1,} & {{{if}\mspace{14mu} {d\left( {s_{1},s_{2}} \right)}} = 0} \\{0,} & {otherwise}\end{matrix} \right.$

with respect to secondary structure.

Although the size of the ensemble, Γ, grows exponentially with thenumber of nucleotides N, the MFE structure having the lowest energy, thepartition function, and the equilibrium base-pairing probabilities canbe evaluated using Θ(N³) dynamic programs.

For a given target structure, s, the determination of the nucleotidesequence that will produce the target structure s can be specified as anoptimization problem, minimizing an objective function with respect tosequence, φ. Rather than seeking a global optimum, optimization can beterminated if the objective function is reduced below a prescribed stopcondition.

One strategy to determine the lowest free energy sequence thatcorresponds to a particular target structure s is to minimize the MFEdefect

$\begin{matrix}{{\mu \left( {\varphi,s} \right)} = {d\left( {s^{MFE},s} \right)}} \\{{= {N - {\underset{1 \leq j \leq {N + 1}}{\sum\limits_{1 \leq i \leq N}}{{S_{i,j}\left( {s^{MFE}(\varphi)} \right)}{S_{i,j}(s)}}}}},}\end{matrix}$

corresponding to the distance between the MFE structure s^(MFE)(φ) andthe target structure s. This approach hinges on whether or not theequilibrium structural features of ensemble Γ are well-characterized bythe single structure s^(MFE)(φ), which in turn depends on the specificsequence φ. If μ(φ, s)=0, the target structure s is the most probablesecondary structure at equilibrium. However, p(φ, s) can nonetheless bearbitrarily small due to competition from other secondary structures inΓ.

To address this concern, an alternative strategy to MFE defectminimization is to minimize the probability defect:

π(φ, s)=1−p(φ, s),

corresponding to the sum of the probabilities of all non-targetstructures in the ensemble Γ. If π(φ, s)≈0, the sequence design isessentially ideal because the equilibrium structural properties of theensemble are dominated by the target structure s. However, as π(φ, s)deviates from zero, it increasingly fails to characterize the quality ofthe sequence because the probability defect treats all non-targetstructures as being equally defective. This property is a concern forchallenging designs where it may be infeasible to achieve π(φ, s)≈0.

To address these shortcomings, still another strategy is to minimize theensemble defect between the target structure s and the equilibriumproperties of sequence φ. The ensemble defect

$\begin{matrix}{{n\left( {\varphi,s} \right)} = {\sum\limits_{\sigma \in \Gamma}{{p\left( {\varphi,\sigma} \right)}{d\left( {\sigma,s} \right)}}}} \\{{= {N - {\underset{1 \leq j \leq {N + 1}}{\sum\limits_{1 \leq i \leq N}}{{P_{i,j}(\varphi)}{S_{i,j}(s)}}}}},}\end{matrix}$

corresponds to the average number of incorrectly paired nucleotides atequilibrium calculated over ensemble Γ.

These three objective functions, ensemble defect minimization, MFEdefect minimization and probability defect minimization, and can be castinto a unified form to highlight their differences:

${{n\left( {\varphi,s} \right)} = {\sum\limits_{\sigma \in \Gamma}{{p\left( {\varphi,s} \right)}{d\left( {\sigma,s} \right)}}}},{{\mu \left( {\varphi,s} \right)} = {\sum\limits_{\sigma \in \Gamma}{\delta_{\sigma,s^{MFE}}{d\left( {\sigma,s} \right)}}}},{{\pi \left( {\varphi,s} \right)} = {\sum\limits_{\sigma \in \Gamma}{{p\left( {\varphi,s} \right)}{\left( {1 - \delta_{\sigma,s}} \right).}}}}$

Using n(φ, s) to perform ensemble defect optimization, the averagenumber of incorrectly paired nucleotides at equilibrium is evaluatedover ensemble Γ using p(φ, a), the Boltzmann-weighted probability ofeach secondary structure σ ∈ Γ, and d(σ, s), the distance between eachsecondary structure σ ∈ Γ and the target structure s. By comparison,using μ(φ, s) to perform MFE defect optimization, p(φ, a) is replaced bythe discrete delta function δ_(σ, s) ^(MFE), which is unity for s^(MFE)and zero for all other structures σ ∈ Γ. Alternatively, using π(φ, s) toperform probability defect optimization, d(σ, s) is replaced by thebinary distance function (1−δ_(σ, s)), which is zero for s and 1 for allother structures σ ∈ Γ.

Hence, the MFE defect makes the optimistic assumption that s^(MFE) willdominate Γ at equilibrium, while the probability defect makes thepessimistic assumption that all structures σ ∈ Γ with d(σ, s)≠0 areequally distant from the target structure s. The objective function n(φ,s) quantifies the equilibrium structural defects of sequence φ even whenμ(φ, s) and π(φ, s) do not.

SUMMARY OF THE INVENTION

Described herein are systems and processes for designing the sequence ofone or more interacting nucleic acid strands intended to adopt a targetsecondary structure at equilibrium. Sequence design is formulated as anoptimization problem with the goal of reducing an ensemble defect belowa user-specified stop condition. For a candidate sequence and a giventarget secondary structure, the ensemble defect is the average number ofincorrectly paired nucleotides at equilibrium evaluated over theensemble of unpseudoknotted secondary structures. To reduce thecomputational cost of accepting or rejecting mutations to a randominitial sequence, candidate mutations are evaluated on leaf nodes of atree-decomposition of the target structure.

As described in more detail below, during leaf optimization,defect-weighted mutation sampling is used to select each candidatemutation position with probability proportional to its contribution tothe ensemble defect of the leaf. As subsequences are merged moving upthe tree, emergent structural defects resulting from crosstalk betweensibling sequences are eliminated via reoptimization within the defectivesubtree starting from new random subsequences. Using a Θ(N³) dynamicprogram to evaluate the ensemble defect of a target structure with Nnucleotides, this hierarchical approach implies an asymptotic optimalitybound on design time. Thus, for sufficiently large N, the cost ofsequence design is bounded below by 4/3 the cost of a single evaluationof the ensemble defect for the full sequence. Hence, the design processhas time complexity Ω(N³). For target structures containing N ∈ {100,200, 400, 800, 1600, 3200} nucleotides and duplex stems ranging from 1to 30 base pairs, RNA sequence designs at 37° C. typically succeed insatisfying a stop condition with ensemble defect less than N/100.Empirically, the sequence design process exhibits asymptotic optimalityand the exponent in the time complexity bound is sharp. Thisdemonstrates a great improvement over prior systems that did notdemonstrate this asymptotic optimality.

An embodiment is an electronic system for optimizing the sequence of anucleic acid strand to adopt a specific target secondary structure atequilibrium. This embodiment of the system includes: an input forreceiving a target secondary structure; a hierarchical structuredecomposition module configured to decompose the target secondarystructure at split points into a tree of parental nodes, child nodes andleaf nodes wherein each split point is formed within a duplex stem ofthe target structure; a leaf optimization module configured to determinea leaf nucleotide sequence of the target structure at each leaf node inthe tree; and a merging module configured to recurse the nodes of thetree and merge the determined leaf nucleotide sequences at each node tooptimize the nucleotide sequence of a nucleic acid strand that adoptsthe specific target secondary structure at equilibrium.

Yet another embodiment is a method in an electronic system for designinga nucleotide sequence to adopt a target secondary structure atequilibrium. This method includes: decomposing a target secondarystructure of a nucleic acid molecule into a tree structure having leavesand nodes, wherein the decomposition takes place at splice points withinduplex stems of the target secondary structure; designing a nucleotidesequence for each leaf within the binary tree; recursing the tree tomerge and reoptimize the nucleotide sequence for each node of the tree;and determining the nucleotide sequence of the root node from the mergedand reoptimized nucleotide sequences of the other nodes in the tree.

One other embodiment is an electronic system for designing a nucleotidesequence to adopt a target secondary structure at equilibrium. In thisembodiment, the system includes means for decomposing a target secondarystructure of a nucleic acid molecule into a tree having leaves andnodes, wherein the decomposition takes place at splice points withinduplex stems of the target secondary structure; means for designing anucleotide sequence for each leaf within the binary tree; means forrecursing the tree to merge and reoptimize the nucleotide sequence foreach node of the tree; and means for determining the nucleotide sequenceof the root node from the merged and reoptimized nucleotide sequences ofthe other nodes in the tree.

Still another embodiment is a programmed storage device havinginstructions that when executed perform a method that includes:decomposing a target secondary structure of a nucleic acid molecule intoa tree having leaves and nodes, wherein the decomposition takes place atsplice points within duplex stems of the target secondary structure;designing a nucleotide sequence for each leaf within the binary tree;recursing the tree to merge and reoptimize the nucleotide sequence foreach node of the tree; and determining the nucleotide sequence of theroot node from the merged and reoptimized nucleotide sequences of theother nodes in the tree.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram depicting certain embodiments of a nucleicacid sequence design system, wherein a sequence is designed for aproposed secondary structure.

FIG. 2 is a schematic illustration of one embodiment of a method forperforming a hierarchical decomposition of a target structure.

FIG. 3 is a flow diagram showing one embodiment of a process fordetermining a nucleotide sequence having a target structure atequilibrium.

FIG. 4 shows pseudocode descriptions of the functions “DesignSeq”,“UpdateChildren” and “OptimizeLeaf' found in certain of the disclosedembodiments.

FIGS. 5 a-5 c are plots of the structural features of test setsgenerated to characterize the nucleic acid design system. FIG. 5 a is ahistogram showing the fraction of bases paired in each structure forboth test sets. FIG. 5 b is a histogram counting the number ofstructures with a given number of stems. FIG. 5 c is a histogram whichshows the number of stems of each length in each test set.

FIGS. 6 a-6 d are plots showing the performance of certain embodimentsof the system as the target structure size is increased. Particularly,FIG. 6 a depicts the median relative ensemble defect after applying thedesign process to the engineered and random test set of targetstructures. FIG. 6 b depicts the median time cost of applying the designprocess to the engineered and a random test set of target structures.FIG. 6 c depicts the relative guanine-cytosine content after applyingthe design process to the engineered and a random test set of targetstructures. FIG. 6 d depicts the design time cost, relative to theobjective function evaluation cost, of applying certain processes to theengineered and a random test set of target structures.

FIGS. 7 a and 7 b depict the degree leaf independence in the designprocess for the engineered and random test set, respectively. They showensemble defects of the leaves vs the root ensemble defects for initialsequences, sequences in which the leaves are designed and sequencesafter merging and reoptimization.

FIGS. 8 a-8 d are plots highlighting the effects of hierarchicalstructure decomposition and defect-weighted sampling on performance ofcertain embodiments on the engineered test set. Particularly, FIG. 8 adepicts the relative ensemble defect after applying embodiments with andwithout hierarchical decomposition and with and without defect-weightedmutation sampling to each of a uniform sampling and defect-weightedsampling dataset for each of single-scale optimization and hierarchicaloptimization methods. FIG. 8 b depicts the time cost of applyingsingle-scale embodiments, hierarchically decomposing embodiments,uniform mutation sampling embodiments and defect-weighted mutationsampling embodiments. FIG. 8 c depicts the relative guanine-cytosinecontent after applying the above embodiments. FIG. 8 d depicts thedesign cost, relative to the objective function evaluation cost, of theembodiments.

FIGS. 9 a-9 d show plots of the effects of sequence initialization onperformance for certain of the embodiments. Particularly, four types ofsequences are considered: entirely random sequences, sequences of randomadenosine-uracil (AU), sequences of random guanine-cytosine (GC), andsequences satisfying sequence symmetry minimization (SSM). FIG. 9 adepicts the relative median ensemble defect resulting from each initialsequence type. FIG. 9 b depicts the time cost resulting from eachinitial sequence type. FIG. 9 c depicts the relative guanine-cytosinecontent resulting from each initial sequence type. FIG. 9 d depicts thedesign cost relative to the objective function evaluation cost,resulting from each initial sequence type.

FIGS. 10 a-10 d show plots of the effects of a selected stop conditionstringency on performance for certain embodiments of the invention.Particularly, five degrees of stop condition stringency are considered:f_(stop)=0.001, f_(stop)=0.005, f_(stop)=0.01, f_(stop)=0.05, andf_(stop)=0.1. Each stringency was tested using the engineered test set.FIG. 10 a depicts the median normalized ensemble defect resulting fromeach of the stop condition stringencies. FIG. 10 b depicts the time costof each of the stop condition stringencies. FIG. 10 c depicts therelative guanine-cytosine content of each of the stop conditionstringencies. FIG. 10 d depicts the design cost, relative to theobjective function cost, resulting from each of the stop conditionstringencies.

FIGS. 11 a-11 d show plots of the effects of single and multi-strandedstructures on performance for certain embodiments of the invention.Structures were selected from the engineered test set. FIG. 11 a depictsthe median ensemble defect for each length of single and multi-strandedstructures. FIG. 11 b depicts the relative time cost resulting from eachof single and multi-stranded structures. FIG. 11 c depicts the relativeguaninecytosine content resulting from each of single and multi-strandedstructures. FIG. 11 d depicts the median design cost, relative to theobjective function cost, resulting from each length of single andmulti-stranded structures.

FIGS. 12 a-12 d show plots of the effects of the selected designmaterial on performance for certain embodiments of the invention.Structures were selected from the engineered test set. FIG. 12 a depictsthe median normalized ensemble defect resulting from each structurelength. FIG. 12 b depicts the normalized median time cost resulting fromeach structure length. FIG. 12 c depicts the guanine-cytosine contentresulting from each structure length. FIG. 12 d depicts the design cost,relative to the objective function cost.

FIGS. 13 a-13 d show plots of the effects of pattern prevention forcertain of the embodiments described herein. Structures were selectedfrom the engineered test set. FIG. 13 a depicts the median normalizedensemble defect resulting from each of unconstrained designs and designswith pattern preventions. FIG. 13 b depicts the relative time costresulting from each of unconstrained designs and designs with patternpreventions. FIG. 13 c depicts the relative guanine-cytosine contentresulting from each of unconstrained designs and designs with patternpreventions. FIG. 13 d depicts the relative design cost, as measured bythe objective function, resulting from each of unconstrained designs anddesigns with pattern preventions.

FIG. 14 a plots the efficiency on engineered test structures whencertain embodiments are parallelized. FIG. 14 b plots the speedup whencertain embodiments are parallelized.

FIGS. 15 a-15 f are plots of the effects of different objectivefunctions and variations on the embodiments described herein,specifically embodiments using single-scale methods to optimize theprobability defect, single-scale methods to optimize the ensembledefect, hierarchical methods to optimize the MFE defect, andhierarchical methods to optimize the ensemble defect. These embodimentsare all tested on the engineered test set. Particularly, FIG. 15 adepicts the median normalized ensemble defect after applying theseembodiments. FIG. 15 b depicts the relative time cost after applyingthese embodiments. FIG. 15 c depicts the relative guanine-cytosinecontent after applying these embodiments. FIG. 15 d depicts the relativedesign cost of applying these embodiments. FIGS. 15 e and 15 f depictdesign quality of the four embodiments, showing the probability defectrelative to the normalized ensemble defect and the normalized MFE defectrelative to the normalized ensemble defect.

DETAILED DESCRIPTION

Embodiments of the invention relate to systems and processes fordesigning nucleic acid molecules that meet a target design criteria. Inone embodiment, a single target unpseudoknotted structure havingpredetermined design criteria is input into the system. The targetstructure may include one or more nucleic acid molecule chains ofunknown sequence. As described in more detail below, the system uses thetarget unpseudoknotted structure to optimize the base composition of oneor more nucleotide sequences that will base pair together into asecondary structure that meets the target design criteria. This systemis useful in order to design nucleic acid molecules that assemble invitro or in vivo into target structures.

In one embodiment, the system is used to design small conditional RNAsthat are used in a hybridization chain reaction (HCR). The HCR isdescribed in U.S. Pat. No. 7,727,721 issued on Jun. 1, 2010, which isincorporated herein by reference in its entirety. These smallconditional RNAs form target secondary structures that changeconformation upon detection of a particular signal (Dirks R M, Pierce NA (2004) Triggered amplification by hybridization chain reaction. ProcNatl Acad Sci USA 101:15275-15278). For example, small conditional RNAshave been designed to self-assemble and disassemble along apredetermined pathway in order to perform dynamic functions (Yin, et al,(2008) Programming biomolecular self-assembly pathways Nature451:318-322). Small conditional RNAs can cause selective cell death ofcancer cells (Venkataramanm, et al, (2010) Selective cell death mediatedby small conditional RNAs, Proc Natl Acad Sci USA 107:16777-16782) andhave been used in multiplexed fluorescent in situ hybridization assaysto map mRNA expression within intact biological samples (Choi, et al.(2010) Programmable in situ amplification for multiplexed imaging ofmRNA expression Nature. Biotechnol. 28:1208-1212). The disclosedembodiments provide a nucleic acid molecule design process and systemthat achieves high design quality while maintaining a low computationaloverhead.

In one example, the system may use separate sub-processes to design apolynucleotide sequence φ of one or more strands that will form into atarget secondary structure s at equilibrium. The system optimizes theensemble defect of this target structure using several primaryprocesses. The three main sub-processes in this embodiment arehierarchical structure decomposition of the target structure, leafoptimization with defect-weighted mutation sampling, and subsequencemerging and reoptimization, each of which is described in more detailbelow. Equilibrium can be at 1M Na⁺, or designated by user-specified Na⁺and Mg⁺⁺ concentrations.

In one embodiment a target nucleotide sequence secondary structure isdefined as a structure matrix S(s) with entries S_(ij)(s) ∈ {0, 1}. Ifstructure s contains pair i·j, then S_(i,j)(s)=1, otherwise S_(ij)(s)=0.The system performs hierarchical structure decomposition on the targetsecondary structure s, defect-weighted sampling on the leaves, andmerging and reoptimization as it traverses up the decomposition tree.The hierarchical structure decomposition identifies stem structureswithin the target structure s, and divides the root structure into atree of substructures. In one embodiment, the tree of substructures is abinary tree of substructures. The minimum number of nucleotides in anode can be set by the user. If a node can be divided into two nodes,both of which contain more than the minimum number of nucleotides, thenthe node is divided. For a given target secondary structure, s, with Nnucleotides, we seek to design a sequence, φ, with ensemble defect, n(φ,s), satisfying the stop condition:

n(φ, s)≦f _(stop) N,

for a user-specified value of f_(stop) ∈ (0, 1). Using ensemble defectanalysis, candidate mutations in a test nucleotide sequence areevaluated at the leaves of the binary tree decomposition of the targetstructure. During leaf optimization, defect-weighted mutation samplingis used to select each candidate mutation position with probabilityproportional to its contribution to the ensemble defect of the leaf.Mutations are accepted if they decrease the leaf ensemble defect.Subsequences are merged moving up the tree and parental ensemble defectsare evaluated. If emergent structural defects are encountered whenmerging subsequences, they are eliminated via defect-weighted childsampling and reoptimization. After all of the subsequences have beenmerged, the final result is a nucleotide sequence φ that has a minimizedensemble defect when folded into the target structure s. Because theminimization of the ensemble defect is performed by recursing nodes andleaves of the binary tree, the process is computationally much moreefficient than prior systems that optimized the objective function overentire nucleotide sequence φ.

Definitions

As used herein, an “input device” can be, for example, a keyboard,rollerball, mouse, voice recognition system or other device capable oftransmitting information from a user to a computer. The input device canalso be a touch screen associated with the display, in which case theuser responds to prompts on the display by touching the screen. The usermay enter textual information through the input device such as thekeyboard or the touch-screen.

The invention is operational with numerous other general purpose orspecial purpose computing system environments or configurations.Examples of well-known computing systems, environments, and/orconfigurations that may be suitable fore use with the invention include,but are not limited to, personal computers, server computers, hand-heldor laptop devices, multiprocessor systems, microprocessor-based systems,programmable consumer electronics, network PCs, minicomputers, mainframecomputers, distributed computing environments that include any of theabove systems or devices.

As used herein, “instructions” refer to computer-implemented steps forprocessing information in the system. Instructions can be implemented insoftware, firmware or hardware and include any type of programmed stepundertaken by components of the system.

A “microprocessor” or “processor” may be any conventional generalpurpose single- or multi-core microprocessor such as a Pentium®processor, Intel® Core™, a 8051 processor, a MIPS® processor, or anALPHA® processor. In addition, the microprocessor may be anyconventional special purpose microprocessor such as a digital signalprocessor or a graphics processor.

The system is comprised of various modules as discussed in detail below.As can be appreciated by one of ordinary skill in the art, each of themodules comprises various sub-routines, procedures, definitionalstatements and macros. Each of the modules are typically separatelycompiled and linked into a single executable program. Therefore, thefollowing description of each of the modules is used for convenience todescribe the functionality of the preferred system. Thus, the processesthat are undergone by each of the modules may be arbitrarilyredistributed to one of the other modules, combined together in a singlemodule, or made available in, for example, a shareable dynamic linklibrary.

The system may be used in connection with various operating systems suchas SNOW LEOPARD®, iOS®, LINUX, UNIX or MICROSOFT WINDOWS®.

The system may be written in any conventional programming language suchas C, C++, BASIC, Pascal, or Java, and run under a conventionaloperating system.

A web browser comprising a web browser user interface may be used todisplay information (such as textual and graphical information) to auser. The web browser may comprise any type of visual display capable ofdisplaying information received via a network. Examples of web browsersinclude Microsoft's Internet Explorer browser, Mozilla's Firefoxbrowser, Apple Safari and PalmSource's Web Browser, or any otherbrowsing or other application software capable of communicating with anetwork.

The invention disclosed herein may be implemented as a method, apparatusor article of manufacture using standard programming or engineeringtechniques to produce software, firmware, hardware, or any combinationthereof. The term “article of manufacture” as used herein refers to codeor logic implemented in hardware or computer readable media such asoptical storage devices, and volatile or non-volatile memory devices.Such hardware may include, but is not limited to, field programmablegate arrays (FPGAs), application-specific integrated circuits (ASICs),complex programmable logic devices (CPLDs), programmable logic arrays(PLAs), microprocessors, or other similar processing devices.

In addition, the modules or instructions may be stored onto one or moreprogrammable storage devices, such as FLASH drives, CD-ROMs, hard disks,and DVDs. One embodiment includes a programmable storage device havinginstructions stored thereon that when executed perform the methodsdescribed herein to determine nucleic acid sequence information.

As used herein, a “node” is a structure which may contain a value, acondition, or represent a separate data structure (which could be a treeof its own). Each node in a tree has zero or more child nodes, which arebelow it in the tree (by convention, trees are drawn growing downwards).A node that has a child is called the child's parent node (or ancestornode, or superior). A node typically has at most one parent. As isknown, nodes that do not have any children are called leaf nodes orterminal nodes.

The topmost node in a tree is the root node. An internal node or innernode is any node of a tree that has child nodes and is thus is notconsidered to be a leaf node. Similarly, an external node or outer nodeis any node that does not have child nodes and is thus a leaf node

A subtree of a tree is a tree consisting of a node in the tree and allof its descendants in the tree. For example, the subtree correspondingto the root node is the entire tree. The subtree that corresponds to anyother node may be called a proper subtree.

System Overview

FIG. 1 shows one embodiment of a system 100 for determining the nucleicacid sequence, or sequences that will form a target structure. Thesystem includes a processor 102 configured to run instructions providedby each module within the system 100. As shown, a target secondarystructure 105 is input into a nucleic acid sequence design system 110.The sequence design system 110 includes a memory 113 and a displayoutput 114. The memory 113 can be any type of conventional RAM or ROMmemory, configured to store, receive, and buffer instructions, data orother information used within the system 110. The display output 114 isconfigured to link with a conventional monitor or other device forshowing textual or graphical information output by the system 110. Inone embodiment, the system 110 is an Internet server and the targetstructure is provided from an Internet user accessing the server.

Also within the nucleic acid design system 110 is a hierarchicalstructure decomposition module 115 that contains instructions fordividing the larger target structure into a memory structure comprisinga binary tree that has nodes and leaves for further analysis. Thus, thehierarchical structure decomposition module 115 provides one means fordecomposing a target nucleic acid structure. Each node or leaf maycorrespond to a stem, or stem fragment of the target structure s. Inorder minimize emergent errors that may occur when the target structuresin the leaf nodes are later rejoined, one or more “dummy” or spacernucleotides can be added to the ends of the structures within each leafso that the leaf structure more accurately mimics the performance of thelarger overall target structure. The memory structure of the binary treemay be stored within the memory 113 of the system 110.

One schematic representation of a method of decomposing a targetstructure 182 is shown in FIG. 2. As shown, the target structure 182includes a stem 184 and loop 186. In a first step in the decomposition,the stem 184 is divided approximately in half at a split point 187 tocreate a first stem portion 188 and a second stem and loop portion 190.A set of two base-paired dummy nucleotides 192 are added to the firststem portion 188. A set of two base-paired dummy nucleotides 193 arethen added to the second stem and loop portion 190. Thus, in thisembodiment, the system adds dummy nucleotides to either side of thesplit point 187. In a second step in the decomposition, the first stemportion 188 is divided approximately in half at a split point 194 toresult in the formation of leaf fragments 188 a and 188 b. Dummynucleotide sets 195 and 196 are added to either side of the split point194. Similarly, at the second step, the stem and loop portion 190 isdivided approximately in half at a split point 197 to yield leaffragments 190 a and 190 b. Dummy nucleotides sets 198 and 199 are addedto either side of the split point 197. This schematic representationthus illustrates one embodiment of a method for decomposing a targetstructure at its stem portions into a tree comprising nodes and leaves.

Referring back to FIG. 1, the system 110 also includes a leafoptimization module 120 that receives data representing the leaves andnodes of the decomposed binary tree. The leaf optimization module 120has instructions for selecting a first random nucleotide sequence to becompared against the decomposed target structure at the selected leaf.Thus, the leaf optimization module provides one means for optimizingnucleic acid sequences in leaf nodes of a hierarchical tree. An ensembledefect optimization module 125 is provided within the system 110, andcontains instructions to use ensemble defect optimization to determinethe ensemble distance of the random nucleotide sequence from the targetstructure.

If the random nucleotide sequence has a normalized ensemble defectgreater than the stop condition, then the leaf optimization module 120also provides instructions for mutating the random nucleotide sequence,and recalculating the ensemble defect of the mutated sequence. Once theleaf optimization module 120 has performed enough rounds of optimizationso that the ensemble defect is below the leaf stop condition, asubsequence merging and reoptimization module 130 is used to merge twoor more optimized leaves into a node. In one embodiment, the dummynucleotides are first removed from each leaf target structure prior tomerger with an upper node. Thus, the subsequence merging andreoptimization module 130 provides one means for recursing the tree tomerge and reoptimize the nucleotide sequence for each node of the tree.

The pair probabilities of the merged nucleotide sequence are thencompared against the target structure at that node to determine whetherthe merged sequences meet a predetermined stop condition or minimum freeenergy. If the stop condition is reached, then the subsequence mergingand reoptimization module recurses down a layer in the binary tree tore-optimize the child sequences prior to merger. If the stop conditionis not reached, then the subsequence merger and reoptimization module130 continues recursing up the binary tree until the entire nucleotidesequence corresponding to a target structure 140 has been determined.Thus, the subsequence merging and reoptimization module also providesone means for determining the nucleotide sequence of the root node ofthe tree from the merged and reoptimized nucleotide sequences of theother nodes in the tree. Additional details on the modules within thesystem 110 are described in more detail with regard to FIG. 3.

Process Flow

Referring now to FIG. 3, a process 200 for determining a minimum freeenergy nucleotide sequence that corresponds to a target structure s isshown. The process 200 starts at a start state 205 and moves to a state210 wherein a target structure is received by the system for analysis.In one embodiment the target nucleotide sequence secondary structure isprovided using dot-parens-plus notation to denote a secondary structure.In this notation, each unpaired base is represented by a dot, each basepair is denoted by matching parentheses, and each nick between strandsby a plus. An additional notation, called HU+allows a user to specifysecondary structures by specifying sizes of structural elements insteadof individual base pairs. In this notation, helices are represented byHx and unpaired regions by Ux, where x represents the size of a helix orunpaired region. Each helix is followed immediately by the substructurethat is “enclosed” by the helix. If this substructure includes more thanone element, parentheses are used to denote scope. A nick betweenstrands is specified by a +. The system 110 can convert this notationinto a structure matrix S(s) with entries S_(ij)(s) ∈ {0, 1}. Ifstructure s contains pair i·j, then S_(ij)(s)=1, otherwise S_(ij)(s)=0.Of course, other formats defining the target structure are alsocontemplated within embodiments of the invention. Alternatively, thetarget structure can be provided directly as a matrix S(s).

Once the target structure s has been received by the system at the state210, the process 200 moves to a state 215 wherein the hierarchicalstructure decomposition module 115 (FIG. 1) is executed to decompose thestructure s into a binary tree. By performing a binary treedecomposition of the target secondary structure s, each parent structurewithin a duplex stem of the test molecule is decomposed into smallernodes on the binary tree. The target structure s may be decomposed intoa balanced, or unbalanced, binary tree of substructures, with each nodeof the tree indexed by a unique integer k. For each parent node, k,there is a left child node, k_(l), and a right child node, k_(r). Eachnucleotide in parent structure s^(k) is partitioned to either the leftor right child substructure (S^(k)=S_(l) ^(k)∪S_(r) ^(k) and S_(l)^(k)∩S_(r) ^(k)=φ). Child node k_(l) inherits from parent node k theaugmented substructure, S_(l) ₊ ^(k), comprising native nucleotides,s_(native) ^(k) ^(l) ≡S_(l) ^(k). The process 100 can them move to state220 where additional dummy nucleotides that approximate the influence ofits sibling in the context of their parent (S^(k) ^(l) ≡S_(native) ^(k)^(l) ∪S_(dummy) ^(k) ^(l) ≡S_(l+) ^(k)). Thus, after decomposing aparticular stem of the target structure, dummy nucleotide pairs can beadded at a state 220 to extend the truncated duplex molecules in eachchild structure to mimic the parental environment.

In contrast to earlier hierarchical methods that decompose parentstructures at multiloops, this hierarchical tree structure decomposesparent structures within duplex stems within the target structure s.Eligible split-points within each stem are those locations within aduplex stem with at least H_(split) consecutive base-pairs to eitherside, such that both children would have at least N_(split) nucleotides.If there are no eligible split-points, a structure becomes a leaf nodein the decomposition tree. Otherwise, an eligible split-point isselected so as to minimize the difference in the size of the children,μS_(l) ^(k)|−|S_(r) ^(k)∥. In order to minimize emergent defectsresulting from crosstalk between child subsequences in one embodiment,dummy nucleotides are introduced into the children to compensate for thefact that the stem has been divided into multiple sequences foranalysis. Dummy nucleotides can be defined by extending the newly-splitduplex stem across the split-point by H_(split base paris (|S) _(dummy)^(k) ^(l) |=2H_(split)).

For a parent node k, the sequence φ^(k) follows the same partitioning asthe structure s^(k) (φ^(k)=φ_(l) ^(k)∪φ_(r) ^(k) and φ_(l) ^(k)∩φ_(r)^(k)=φ). Likewise, for a child node k_(l), the sequence may contain bothnative and dummy nucleotides (φ^(k) ^(l) ≡φ_(native) ^(k) ^(l)∩φ_(dummy) ^(k) ^(l) ≡φ_(l+) ^(k)).

Once the target secondary structure has been decomposed into nodes andleaves, and dummy nucleotides added where necessary, the process 200moves to a state 225 wherein the root node of the binary tree isselected for analysis.

Leaf Optimization with Defect-Weighted Mutation Sampling

Once the first node for analysis has been selected at the state 225, theprocess 200 moves to a state 230 to determine whether to performdefect-weighted mutation sampling or to further recurse in thedecomposition tree. If the node is a leaf, the process 200 moves toperform defect-weighted mutation sampling (states235,240,250,255,260,265,270). If the node is not a leave, the process200 recursively optimizes the left, then the right child nodes.

If a determination was made at the decision state 230 that the currentnode was a leaf, then defect-weighted mutation sampling starts from arandom initial sequence at a state 235. The process 200 then moves to adecision state 240 to determine if a leaf stop condition has beensatisfied. If the current sequence satisfies the stop condition,optimization stops and the process 200 moves to a state 320 to decidewhether the current node is the root node. However, if a determinationis made at the decision state 240 that the leaf stop condition has notbeen satisfied, then a mutation position is chosen using defect-weightedmutation sampling in a state 250. The process 200 then evaluates theensemble defect at a state 255. The process 200 then moves to a decisionstate 260 to determine whether to accept the mutation or not. If theensemble defect is found to have decreased due to the mutation, then theprocess moves to a state 265 and the mutation is accepted and theprocess returns to state 240 to determine if a leaf stop condition hasbeen satisfied. However, if the ensemble defect is not found to havedecreased at the decision state 260, then the process moves to a state270, the mutation is rejected, and the process returns to state 240 todetermine if a leaf stop condition has been satisfied.

In order to calculate the average number of mispaired nucleotides in thetest nucleotide sequence in comparison to the target structure, thestate 255 may call the ensemble defect evaluation module 125 (FIG. 1).The design objective function is the ensemble defect, n(φ, s),representing the average number of incorrectly paired nucleotides of thetest nucleotide sequence at equilibrium calculated over the ensemble ofunpseudoknotted secondary structures Γ. For a target leaf structure withN nucleotides, we seek to satisfy the stop condition: n(φ, s)≦f_(stop)N.As described in more detail below, ensemble defect optimization iscalculated by minimizing n(φ, s) with respect to sequence φ.

After analyzing the initial random test nucleotide sequence, a sequenceoptimization process is performed by the leaf optimization module 120for each leaf node using defect-weighted mutation sampling in which eachcandidate mutation position is selected with probability proportional toits contribution to the ensemble defect of the leaf

At leaf node k, sequence optimization is performed by mutating eitherone base at a time S_(i,|s) _(k) _(|+1) ^(k)=1) or one base pair at atime (if S_(i,j) ^(k)=1 for some 1≦j≦|s^(k)|, in which case φ_(i) ^(k)and φ_(j) ^(k) are mutated simultaneously so as to remain Watson-Crickcomplements).

Defect-weighted mutation sampling by selecting nucleotide i as acandidate for mutation with probability n_(i) ^(k)/n^(k). A candidatesequence {circumflex over (φ)}^(k) is evaluated via calculation of{circumflex over (n)}^(k) if the candidate mutation, ξ, is not in theset of previously rejected mutations, γ_(unfavorable) (position andsequence). A candidate mutation is retained if {circumflex over(n)}^(k)<n^(k) and rejected otherwise. The set, γ_(unfavorable), isupdated after each unsuccessful mutation and cleared after eachsuccessful mutation.

Optimization of leaf k at state 240 terminates successfully if the leafstop condition:

n ^(k) ≦f _(stop) |s ^(k)|

is satisfied, or restarts if M_(unfavorable)|s^(k)| consecutiveunfavorable candidate mutations are either in unfavorable or areevaluated and added to γ_(unfavorable). Leaf optimization is attemptedfrom new random initial conditions up to M_(leafopt) times beforeterminating unsuccessfully. The outcome of leaf optimization is the leafsequence φ^(k) corresponding to the lowest encountered value of the leafensemble defect n^(k).

After a leaf optimization has been performed for a first leaf of aselected node at the state 240, the process 200 moves to the decisionstate 320 to determine if the current node is a root node, and therebythe process can move up the decomposition tree. If a determination wasmade at the decision state 320 that the current node is not a root node,then the process 200 moves to a state 315 to set the current node to theparent of the current node. The process then moves to a state 275 todetermine if the nucleotide sequence at the left child node of theparent has been designed. If the nucleotide sequence has not beendesigned, then the process sets the current node to the left child at astate 305 and moves to decision state 230 to determine if the currentnode is a leaf node.

However, if the nucleotide sequence of the left child node has beendesigned at the decision state 275, then the process 200 moves to adecision state 280 to determine if the right child has been designed. Ifthe right child has not been designed, then the current node is set tothe right child node at a state 310 and the process moves to thedecision state 230 to again determine if the current node is a leaf

If a decision is made that the right child has been designed at thedecision state 280 then the process 200 moves to a state 285 to evaluatethe ensemble defect of the current parental node.

Subsequence Merging and Reoptimization

Thus, once the leaves of a selected node have been optimized, they aremerged at a state 285 so that the merged sequence can be analyzed forconformance with the target structure. As subsequences are merged movingup the tree, each parent node can call the subsequence merging andreoptimization module 130 to initiate defect-weighted child sampling andreoptimization within its sub-tree if there are emergent defectsresulting from crosstalk between child subsequences.

Thus, after evaluation of the ensemble defect of the parental node atthe state 285, the process 200 moves to a decision state 290 todetermine if the merge sequences from the leaf nodes satisfy a stopcondition. If a decision is made that the merged sequence does notsatisfy a stop condition, then the process moves to a state 295 toperform defect-weighted child sampling and re-optimization. Afterreaching the leave by recursively performing defect-weighted childsampling, the process 200 then returns to the state 235 to perform leafoptimization. The merging and reoptimization begins again at thetermination of the leaf optimization.

Leaf reoptimization at state 235 starts from a new random initial testnucleotide sequence. After sibling nodes k^(l) and k_(r) have beenoptimized, parent node k may merge their native subsequences (settingφ_(l) ^(k)=φ_(native) ^(k) ^(l) and φ_(r) ^(k)=φ_(native) ^(k) ^(r) )and evaluate n^(k) to check for a parental stop condition:

n ^(k)≦max(f _(stop) |s _(l) ^(k) |,n _(native) ^(k) ^(l) )+max(f_(stop) |s _(r) ^(k) |,n _(native) ^(k) ^(r) ).

For any node k with sequence φ^(k) and structure s^(k), the ensembledefect, n^(k)≡n(φ^(k), s^(k)), may be expressed as

${n^{k} = {\sum\limits_{1 \leq i \leq {s^{k}}}n_{i}^{k}}},{where}$$n_{i}^{k} = {1 - {\sum\limits_{1 \leq j \leq {{s^{k}} + 1}}{P_{i,j}^{k}{S_{i,j}^{k}.}}}}$

is the contribution of nucleotide i to the ensemble defect of the node.For a parent node k, the ensemble defect can be expressed as a sum ofcontributions from bases partitioned to the left and right childrenn^(k)=n_(l) ^(k)+n_(r) ^(k). For a child node k_(l), the ensemble defectcan be expressed as sum of contributions from native and dummynucleotides (n^(k) ^(l) =n_(native) ^(k) ^(k) +n_(dummy) ^(k) ^(l) ).Conceptually n_(native) ^(k) ^(l) , the contribution of the nativenucleotides to the ensemble defect of child k_(l) (calculated on childnode k₁ at cost Θ(|s^(k) ^(l) |³), approximates n_(l) ^(k), thecontribution of the left-child nucleotides to the ensemble defect ofparent k (calculated on parent node k at higher cost Θ(|s^(k)|³)). Ingeneral, n_(native) ^(k) ^(l) ≠n_(l) ^(k), because the dummy nucleotidesin child node k_(l) only approximate the influence of its sibling (whichis fully accounted for only in the more expensive calculation on parentnode k).

Failure to satisfy the stop condition at decision state 290 implies theexistence of emergent defects resulting from crosstalk between the twochild sequences. In this case, parent node k initiates defect-weightedchild sampling and reoptimization within its subtree. Left child k_(l)is selected for reoptimization with probability n_(l) ^(k)/n^(k) andright child k_(r) is selected for reoptimization with probability n_(r)^(k)/n^(k). This defect-weighted child sampling procedure is performedrecursively until a leaf is encountered (each time using partitioneddefect information inherited from the parent k that initiated thereoptimization). The standard leaf optimization procedure is thenperformed starting from a new random initial sequence. The use of randominitial conditions during leaf reoptimization is based on the assumptionthat sequence space is sufficiently rich that emergent defects cantypically be eliminated simply by designing a different leaf sequence.Following leaf reoptimization, merging begins again starting with thereoptimized leaf and its sibling. The elimination of emergent defects inparent k by defect-weighted child sampling and reoptimization isattempted up to M_(reopt) times.

If this stop condition is satisfied, the process 200 moves to a state320 to determine if the last node in the binary tree has been analyzed.This allows the subsequence merging to continue up the tree. If the lastnode in the tree has not been analyzed, the process 200 moves to a state315 wherein the nucleotide sequence at the next node up or across thetree is selected for design or processing. The process 200 then returnsto state 275 to determine if the nucleotide sequence at the left childnode has been designed. If the last node has been analyzed, then theprocess 200 stops at an end state 325.

The utility of hierarchical structure decomposition reflects theassumption that sequence space is sufficiently rich that twosubsequences optimized for sibling substructures will often not exhibitcrosstalk when merged by a parent node. This hierarchical mutationprocedure is designed to benefit from this property when it holds true,and to eliminate emergent defects when they do arise.

One embodiment of this entire design process is detailed in pseudocodeshown in FIG. 3. As shown in the pseudocode of FIG. 4, for a giventarget structure s, a designed sequence 0 is returned by the functioncall DESIGNSEQ(Ø, s, Ø, 1). During the recursive design procedure, φ, s,and n are local variables that are used to push sequence, structure, anddefect information between nodes in the tree. By contrast, n^(k,a)provides global storage for the ensemble defect of each node k. For agiven k, the index, a=1, . . . , DEPTH(k), enables storage of theensemble defect corresponding to the sequence for node k that has beenaccepted up to depth a in the tree. Storage of these historical valueseliminates unnecessary recalculation of ensemble defects during subtreereoptimization.

Optimality Bound and Time Complexity

This hierarchical sequence design approach implies an asymptoticoptimality bound on the cost of designing the full sequence relative tothe cost of evaluating a single candidate mutation on the full sequence.For a target structure with N nucleotides, evaluation of a candidatesequence requires calculation of n(φ, s) at cost c_(eval)(N)=Θ(N³).Performing sequence design using hierarchical structure decomposition,mutations are evaluated at the leaf nodes and merged subsequences areevaluated at all other nodes. For node k, the evaluation cost isc_(eval)(|s^(k)|). If at least one mutation is required in each leaf,the design cost is minimized by maximizing the depth of the binary tree.Furthermore, at each depth in the tree, the design cost is minimized bybalancing the tree. Hence, a lower bound on the cost of designing thefull sequence is given by

c _(des)(N)≧c _(eval)(N)[1+2(½)³+4(¾)³+8(⅛)³+. . . ]

or

c _(des)(N)≧4/3c _(eva)(N).

Hence, if the sequence design process performs optimally for large N, wewould expect the cost of full sequence design to be 4/3 the cost ofevaluating a single mutation on the full sequence. In practice, manyfactors might be expected to undermine optimality: imperfect balancingof the tree, the addition of dummy nucleotides in each non-root node,the use of finite tree depth, leaf optimizations requiring evaluation ofmultiple candidate mutations, and reoptimization to eliminate emergentdefects. This optimality bound implies time complexity Ω(N³) for thesequence design process.

The systems and processes described above relate to a single-objectivesequence design where the goal was to design the sequence of one or moreinteracting nucleic acid strands intended to adopt a target secondarystructure at equilibrium. Thus, the sequence design was formulated as anoptimization problem with the objective of reducing the ensemble defectbelow a user-specified stop condition. For a candidate sequence, φ, anda target secondary structure, s, the ensemble defect, n(φ, s), is theaverage number of incorrectly paired nucleotides at equilibriumevaluated over ensemble Γ. As described above, for a target secondarystructure with N nucleotides, the above process seeks to satisfy

n(φ, s)≦f ^(stop) N

with f^(stop)=0.01.

A single-objective design job is specified by entering a targetsecondary structure into the nucleic acid sequence design system 110(FIG. 1). In one embodiment, the structure is entered into the systemusing dotparens-plus notation, each unpaired base is represented by adot, each base pair by matching parentheses, and each nick betweenstrands by a plus.

Target Structure Test Sets

Process performance was evaluated on structure test sets containing 30target structures for each of N ∈ {100, 200, 400, 800, 1600, 3200}. Anengineered test set was generated by randomly selecting structuralcomponents and dimensions from ranges intended to reflect currentpractice in engineering nucleic acid secondary structures. Amulti-stranded version was produced by introducing nicks into thestructures in the engineered test set. Each structure in a random testset was obtained by calculating an MFE structure of a different randomRNA sequence at 37° C. FIG. 5 compares the structural features of theengineered and random test sets. In general, the random test set hastarget structures with a lower fraction of bases paired, more duplexstems, and shorter duplex stems (as short as one base pair).

Other Processes

To illustrate the roles of hierarchical structure decomposition anddefect-weighted sampling in the context of ensemble defect optimization,we compared our process to three alternative processes lacking either orboth of these features:

-   -   Single-scale ensemble defect optimization with uniform mutation        sampling.

The leaf optimization process is applied directly on the full sequenceusing uniform mutation sampling in which each candidate mutationposition is selected with equal probability.

-   -   Single-scale ensemble defect optimization with defect-weighted        mutation sampling. The leaf optimization process is applied        directly on the full sequence.    -   Hierarchical ensemble defect optimization with uniform mutation        sampling. The hierarchical process is applied using uniform        mutation sampling during leaf optimization and uniform child        sampling during subsequence merging and reoptimization.

We also modified our process to compare performance to processes alreadyknown by others:

-   -   Single-scale probability defect optimization with uniform        mutation sampling. This method seeks to design a sequence such        that the probability defect satisfies the stop condition π(φ,        s)≦f_(stop). Satisfaction of this stop condition is sufficient        to ensure that stop conditions n(φ, s)≦f_(stop)N and μ(φ,        s)≦f_(stop)N are also satisfied. Optimization is performed using        a modified version of the leaf optimization process (with        π( , s) taking the role of n(φ, s)) applied directly on the full        sequence using uniform mutation sampling.    -   Hierarchical MFE defect optimization with weighted mutation        sampling. This method seeks to design a sequence such that the        MFE defect satisfies the stop condition μ(φ, s)≦f_(stop)N.        Optimization is performed using a modified version of our        process with μ^(k) taking the role of n^(k).

The sequence design process was coded in the C programming language. Byparallelizing the dynamic program for evaluating P(φ) using MPI,²⁶ thesequence design process reduced run time using multiple cores. For adesign job allocated M computational cores, each evaluation of P^(k) fornode k with structure s^(k) is performed using m cores for some m ∈ 1, .. . , M selected to approximately minimize run time based on |s^(k)|.

Results

Our primary test scenario was RNA sequence design at 37° C. for targetstructures in the engineered test set. For each target structure in atest set, 10 independent design trials were performed. Each plotted datapoint represents a median over 300 design trials (10 trials for each of30 structures for a given size N).

Process Performance and Asymptotic Optimality

FIG. 6 demonstrates the typical performance of our process across arange of values of N using the engineered and random test sets. Typicaldesigns surpassed the desired design quality (n(φ, s)≦N/100) as a resultof overshooting stop conditions lower in the decomposition tree (FIG. 6a). For the engineered test set, typical design cost ranges from afraction of a second for N=100 to roughly three hours for N=3200 (FIG. 6b). For small N, the design cost for the random test set is higher thanfor the engineered test set, becoming comparable as N increases. TypicalGC content is less than 60% (starting from random initial sequences with≈50% GC content; FIG. 6 c). Remarkably, as the depth of thedecomposition tree increased with N, the relative cost of design,c_(des)(N)/c_(eval)(N), decreased asymptotically to the optimal bound of4/3 (FIG. 6 d). Hence, for sufficiently large N, the typical cost ofsequence design is only 4/3 the cost of a single mutation evaluation onthe root node. Mutation evaluation had time complexity Θ(N³) and wasempirically observed to be approximately in the asymptotic regime.Hence, for our design process, the empirical observation of asymptoticoptimality demonstrates that the exponent in the Ω(N³) time complexitybound is sharp.

Leaf Independence and Emergent Defects

FIG. 7 compares the ensemble defect evaluated at the root node, to thesum of the ensemble defects evaluated at the leaf nodes. If theassumption of leaf independence is valid (i.e., if dummy nucleotides areeffective at mimicking parental environments and there is minimalcrosstalk between merged subsequences), we would expect the data to fallnear the diagonal.

For the engineered test set (FIG. 7 a), we observed three strikingproperties. First, for random initial sequences, the assumption of leafindependence was well-justified despite the fact that the ensembledefect is large. Second, leaf optimization followed by merging withoutreoptimization (i.e., M_(reopt)=0) typically yielded full sequencedesigns that achieved the desired design quality (n(φ, s)≦N/100 on theroot), with emergent defects arising only in a minority of cases. Third,these emergent defects were successfully eliminated by defect-weightedchild sampling and reoptimization starting from new random initialsubsequences. The resulting full sequence designs exhibited leafindependence and satisfied the stop condition.

By comparison, for the random test set, merging of leaf-optimizedsequences typically did lead to emergent defects in the root node. Evenin this case, our process was found to successfully eliminate emergentdefects using defect-weighted child sampling and reoptimization startingfrom new random initial subsequences.

Contributions of Processic Ingredients

FIG. 8 shows the isolation of contributions of hierarchical structuredecomposition and defect-weighted sampling to the ensemble defectoptimization process by comparing performance to three modifiedprocesses lacking one or both ingredients. All four methods typicallyachieved the desired design quality, with hierarchical methodssurpassing the quality requirement for the root node as a result ofovershooting stop conditions lower in the decomposition tree.Hierarchical methods dramatically reduced design costs relative to theirsingle-scale counterparts (which were not tested for N=800 due to highcost). Defect-weighted sampling reduced design cost and GC content byfocusing mutation effort on the most defective subsequences. For thesingle-scale methods, the relative cost of design,c_(des)(N)/c_(eval)(N), increases with N. For hierarchical methods,c_(des)(N)/c_(eval)(N) decreases asymptotically to the optimal bound of4/3 as N increases. Our process thus combined the design quality ofensemble defect optimization, the reduced cost and asymptotic optimalityof hierarchical decomposition, and the reduced cost and reduced GCcontent of defect-weighted sampling.

Sequence Initialization

To explore the effect of sequence initialization on typical designquality and cost, we tested four types of initial conditions (FIG. 9):random sequences (default), random sequences using only A and T bases,random sequences using only G and C bases, and sequences satisfyingsequence symmetry minimization (SSM). The desired design quality wasachieved independent of the initial conditions (FIG. 9 a), which hadlittle effect on design cost (FIGS. 9 b and 9 d). Designs initiated withrandom AT sequences or with random GC sequences illustrated that theensemble defect stop condition can be satisfied over a broad range of GCcontents (FIG. 9 c).

Stop Condition Stringency

FIG. 10 depicts typical process performance for five different levels ofstringency in the stop condition: f_(stop) ∈ {0.001, 0.005,0.01(default), 0.05, 0.10}. For each stop condition, the observed designquality was better than required (resulting from overshooting stopconditions lower in the decomposition tree). Consistent with empiricalasymptotic optimality, the design cost was independent of f_(stop) forsufficiently large N (for the tested stringency levels). It isnoteworthy that the process was found to be capable of routinely andefficiently designing sequences with ensemble defects of less thanN/1000.

Multi-Stranded Target Structures

Multi-stranded target structures arise frequently in engineeringpractice. FIG. 11 demonstrated that our process performs similarly on asingle-stranded or multi-stranded target structure.

Design Material

FIG. 12 compares RNA and DNA design. DNA designs are performed in 1 M Naat 23° C. to reflect that DNA systems are typically engineered for roomtemperature studies. In comparison to RNA design, DNA design leads tosimilar design quality (FIG. 12 a), higher design cost (FIG. 12 b), andsomewhat higher GC content (FIG. 12 c), while continuing to exhibitasymptotic optimality (FIG. 12 d).

Sequence Constraints and Pattern Prevention

Molecular engineers sometimes constrain the sequence of certainnucleotides in the target structure (e.g., to ensure complementarity toa specific biological sequence), or prevent certain patterns fromappearing anywhere in the design (e.g., GGGG). Our process acceptssequence constraints and pattern prevention requirements expressed usingstandard nucleic acid codes. FIG. 13 demonstrates that the prevention ofpatterns {AAAA, CCCC, GGGG, UUUU, KKKKKK, MMMMMM, RRRRRR, SSSSSS,YYYYYY} had little effect on design quality or GC content (FIGS. 13 aand 13 c), and somewhat increases design cost while retaining asymptoticoptimality (FIGS. 13 b and 13 d).

Parallel Efficiency and Speedup

The contour plots of FIG. 14 demonstrate the parallel efficiency andspeedup achieved using a parallel implementation of the design processon M computational cores (efficiency (N, M)=t(N, 1)/(t(N, M)×M), speedup(N, M)=t(N, 1)/t(N, M), where t is wall clock time). Using twocomputational cores, the parallel efficiency exceeds ≈0.9 for targetstructures with N>400. Using 32 computational cores, the parallelspeedup is 14 for target structures with N=3200.

Comparison to Previous Approaches

FIG. 15 compares the performance of the described process to theperformance of processes discussed by others. Single-scale methods thatemployed uniform mutation sampling to optimize either ensemble defect orprobability defect achieved the desired design quality at significantlyhigher cost and with significantly higher GC content (FIGS. 15 a-c).Sequences resulting from probability defect optimization typicallysurpass the ensemble defect stop condition despite failing to satisfythe probability defect stop condition (FIG. 15 e), reflecting thepessimism of π(φ, s) in characterizing the equilibrium structural defectover ensemble Γ. For either single-scale method, the relative cost ofdesign, c_(des)(N)/c_(eval)(N), increases with N (FIG. 15 d). Owing tothe high cost of the single-scale approaches, designs were not attemptedfor large N.

By contrast, hierarchical MFE defect optimization with defect-weightedsampling led to efficient satisfaction of the MFE stop condition (FIGS.15 b and 15 f), exhibiting asymptotic optimality withc_(des)(N)/c_(eval)(N) approaching 4/3 for large N (FIG. 15 d).Asymptotically, the cost of hierarchical MFE optimization relative tohierarchical ensemble defect optimization is lower by a constant factorcorresponding to the relative cost of evaluating the two objectivefunctions using Θ(N³) dynamic programs (FIGS. 15 b and 15 d). Theshortcoming of MFE defect optimization is the unreliability ofs^(MFE)(φ) in characterizing the equilibrium structural properties ofensemble Γ. Despite satisfying the MFE defect stop condition, sequencesdesigned via MFE defect optimization typically fail to achieve theensemble defect stop condition by roughly a factor of five for theengineered test set (FIG. 15 a), and by roughly a factor of 20 for therandom test set.

Summary of Results

Using a Θ(N³) dynamic program to evaluate the design objective function,we derived an asymptotic optimality bound on design time: for large N,the minimum cost to design a sequence with N nucleotides was 4/3 thecost of evaluating the objective function once on N nucleotides. Hence,our design process has time complexity Ω(N³).

We studied the performance of our process in the context of empiricalsecondary structure free energy models that have practical utility forthe analysis and design of functional nucleic acid systems. Inparticular, we examined RNA design at 37° C. on target structurescontaining N ∈ {100, 200, 400, 800, 1600, 3200} nucleotides and duplexstems ranging from 1 to 30 base pairs. Empirically, we observe severalstriking properties. For example, emergent defects were sufficientlyinfrequent that they were typically eliminated by leaf reoptimizationstarting from new random initial sequences. In addition, it was routineto design sequences with ensemble defect n(φ, s)<N/100 over a wide rangeof GC contents. Additionally, the process described herein exhibitedasymptotic optimality for large N, with full sequence design costingroughly 4/3 the cost of a single evaluation of the objective function.Hence, the process was efficient in the sense that the exponent in theΩ(N³) time complexity bound is sharp.

While the above processes and methods are described above as includingcertain steps and are described in a particular order, it should berecognized that these processes and methods may include additional stepsor may omit some of the steps described. Further, each of the steps ofthe processes does not necessarily need to be performed in the order itis described.

While the above description has shown, described, and pointed out novelfeatures of the invention as applied to various embodiments, it will beunderstood that various omissions, substitutions, and changes in theform and details of the system or process illustrated may be made bythose skilled in the art without departing from the spirit of theinvention. As will be recognized, the present invention may be embodiedwithin a form that does not provide all of the features and benefits setforth herein, as some features may be used or practiced separately fromothers.

The steps of a method or algorithm described in connection with theembodiments disclosed herein may be embodied directly in hardware, in asoftware module executed by a processor, or in a combination of the two.A software module may reside in RAM memory, flash memory, ROM memory,EPROM memory, EEPROM memory, registers, hard disk, a removable disk, aCD-ROM, or any other form of storage medium known in the art. Anexemplary storage medium is coupled to the processor such the processorcan read information from, and write information to, the storage medium.In the alternative, the storage medium may be integral to the processor.The processor and the storage medium may reside in an ASIC. The ASIC mayreside in a user terminal. In the alternative, the processor and thestorage medium may reside as discrete components in a user terminal.

What is claimed is:
 1. A method in an electronic system for determininga nucleotide sequence that can adopt a target secondary structure atequilibrium, comprising: decomposing a target secondary structure of anucleic acid molecule into a binary tree structure having leaves andnodes, wherein the decomposition takes place at splice points withinduplex stems of the target secondary structure; designing a nucleotidesequence for each leaf within the binary tree; recursing the binary treeto merge and reoptimize the nucleotide sequence for each node of thebinary tree; and determining the nucleotide sequence of the root nodefrom the merged and reoptimized nucleotide sequences of the other nodesin the binary tree.
 2. The method of claim 1, wherein decomposing thetarget secondary structure comprises adding dummy nucleotides to thesplice points of the decomposed target secondary structure.
 3. Themethod of claim 2, wherein recursing the tree comprising removing thedummy nucleotides when merging the leaves into parent nodes.
 4. Themethod of claim 1, wherein decomposing the target secondary structurecomprising setting splice points at least a minimum number of base pairsfrom the end of the duplex stems.
 5. The method of claim 1, whereindesigning the nucleotide sequence for each leaf comprises optimizing thenucleotide sequence of the leaf nodes of the tree to reduce an ensembledefect of each leaf node below a user-specified stop condition.
 6. Themethod of claim 5, wherein optimizing the nucleotide sequence comprisesoptimizing the ensemble defect using defect weighted mutation samplingso that a candidate mutation position in the nucleotide sequence israndomly selected with a probability proportional to the ensemble defectcontribution of the nucleotide.
 7. The method of claim 1, whereinrecursing the binary tree comprises identifying defective subtrees ofthe tree and re-optimizing any defective subtree by defect-weightedchild sampling, wherein a child node is randomly selected forre-optimization with a probability that is proportional to the ensembledefect contribution of the child node.
 8. The method of claim 1, whereindesigning the nucleotide sequence for each leaf within the binary treecomprises selecting an initial random nucleotide sequence to be comparedagainst the decomposed target secondary structure at each leaf node inthe tree.
 9. The method of claim 8, wherein the initial randomnucleotide sequence is iteratively mutated to optimize the nucleotidesequence at each leaf of the tree.
 10. The method of claim 1, whereinthe method is implemented in computer servers.
 11. The method of claim1, wherein the target secondary structure comprises one or more nucleicacid chains.
 12. The method of claim 1, wherein the target secondarystructure is converted into a matrix within a computer memory.
 13. Themethod of claim 1, wherein decomposing the target secondary structurefurther comprises identifying stem structures within the targetsecondary structure.
 14. The method of claim 1, wherein decomposing thetarget secondary structure further comprises decomposing the parentalnodes until all the nodes are leaf nodes.
 15. The method of claim 1,wherein determining the nucleotide sequence of the root node furthercomprises providing a nucleotide sequence of a nucleic acid strand thatadopts the target secondary structure.
 16. An electronic system fordetermining a nucleotide sequence that adopts a target secondarystructure at equilibrium, comprising: means for decomposing a targetsecondary structure of a nucleic acid molecule into a binary tree havingleaves and nodes, wherein the decomposition takes place at splice pointswithin duplex stems of the target secondary structure; means fordesigning a nucleotide sequence for each leaf within the binary tree;means for recursing the tree to merge and reoptimize the nucleotidesequence for each node of the tree; and means for determining thenucleotide sequence of the root node from the merged and reoptimizednucleotide sequences of the other nodes in the binary tree.
 17. Aprogrammed storage device comprising instructions that when executed bya processor perform a method comprising: decomposing a target secondarystructure of a nucleic acid molecule into a tree having leaves andnodes, wherein the decomposition takes place at splice points withinduplex stems of the target secondary structure; designing a nucleotidesequence for each leaf within the binary tree; recursing the tree tomerge and reoptimize the nucleotide sequence for each node of the tree;and determining the nucleotide sequence of the root node from the mergedand reoptimized nucleotide sequences of the other nodes in the tree. 18.The programmed storage device of claim 17, wherein the programmedstorage is a compact disk or DVD.
 19. The programmed storage device ofclaim 17, wherein designing the nucleotide sequence for each leafcomprises optimizing the nucleotide sequence of the leaf nodes of thetree to reduce an ensemble defect of each leaf node below auser-specified stop condition.
 20. The programmed storage device ofclaim 17, wherein recursing the tree comprises identifying defectivesubtrees of the tree and re-optimizing any defective subtree bydefect-weighted child sampling, wherein a child node is randomlyselected for re-optimization with a probability that is proportional tothe ensemble defect contribution of the child node.